Manager of Data Science, GoFundMe

• 8+ years of experience in data science roles with direct impact on marketing, growth, or revenue optimization
• Master’s or Ph.D. in a quantitative field (Statistics, Mathematics, Economics, Computer Science, Physics, Operations Research or related), or equivalent applied experience
• Advanced proficiency in Python (NumPy, pandas, scikit-learn) and SQL (window functions, optimization)
• Deep experience with experimentation frameworks: A/B testing, causal inference, uplift modeling, and attribution models
• Proven success in forecasting, optimization, and budget allocation models for marketing and growth functions
• Hands-on with data platforms (Snowflake, Databricks) and BI tools (Looker, Tableau, or equivalent)
• Strong data storytelling and executive presentation abilities
• Exceptional communication skills with the ability to influence executive stakeholders and translate data into actionable business recommendations
• Experience developing senior data scientists and elevating team practices
• Demonstrated ability to define a strategic vision for applied data science in marketing, balancing rapid experimentation with long-term infrastructure investments

Desirable

• We’re looking for a Manager, Data Science to architect and lead the next generation of marketing data science at GoFundMe
• This role will build and scale the foundations of applied data science and AI that empower our Marketing, Growth, and Finance teams to make high-confidence, ROI-positive investment decisions
• You’ll serve as a player-coach for a talented group of experienced data scientists, driving innovation while ensuring excellence in delivery
• Build a strong AI and data science foundation: Develop scalable pipelines, reusable modeling frameworks, and robust experimentation platforms to support marketing and growth decision-making
• Lead end-to-end data science & AI projects: From requirements gathering through feature engineering, modeling, validation, deployment, and monitoring
• Establish best practices: Champion standards in model governance, reproducibility, data quality, and system reliability to ensure sustainable and trustworthy AI adoption
• Drive marketing science innovation: Apply advanced methods—causal inference, uplift modeling, multi-touch attribution, and media mix modeling—to unlock insights and optimize spend
• Advance forecasting & ROI modeling: Deliver budget allocation frameworks and predictive models that guide long-term roadmap planning and marketing efficiency
• Partner cross-functionally: Work closely with Marketing, Growth, Product, Engineering, and Finance leaders to align analytics initiatives with revenue impact
• Invest in people: Mentor, coach, and elevate a team of high-performing data scientists; foster a culture of technical rigor, curiosity, and applied innovation
• Push the frontier of applied AI in marketing: Evaluate emerging generative and predictive AI approaches for audience segmentation, creative optimization, personalization, and campaign efficiency